Electronic systems and circuits have made a significant contribution towards the advancement of modern society and are utilized in a number of applications to achieve advantageous results. Numerous electronic technologies such as digital computers, calculators, audio devices, video equipment, and telephone systems have facilitated increased productivity and reduced costs in analyzing and communicating data, ideas and trends in most areas of business, science, education and entertainment. Frequently, electronic systems designed to provide these advantageous results are realized through the leveraged utilization of centralized resources by distributed network nodes. While leveraged utilization of centralized resources is usually advantageous, allocation of centralized resource operations is usually very complex and often involves power consumption and heat dissipation limitations.
Centralizing certain resources within a distributed network typically provides desirable benefits. For example, centrally storing and/or processing information typically relieves the necessity of wasteful duplicative storage and/or processing resources at each remote networked node. However, managing large storage and processing capabilities of centralized resources is very complex and expensive. Clients interested in engaging a host to provide centralized resources and services typically have a desire to avoid providing the infrastructure, operation and maintenance directly themselves.
Centralized computing resource centers (e.g., server farms, Application Service Provider Centers, Internet Data Centers, Utility Data Centers, etc.) usually include a variety of equipment related to information processing mounted in racks. For example, a rack can include servers, routers, disk arrays, and operational support components (e.g., power distribution components, fans, etc.). The racks usually provide a convenient and efficient way to arrange computing equipment in a centralized operation location. The configuration of the rack structures usually follow conventional standards. However, the equipment mounted within a rack can vary dramatically and this diversity in rack equipment usually gives rise to a number operational concerns. For example, the different rack equipment also usually have different operating characteristics that can have various impacts on centralized resource operations.
In addition to operational characteristic differences, rack equipment are also often utilized to perform a variety of different applications. For example, some applications are associated with low priority tasks and/or do not require significant performance support. Other applications have a high priority and/or require significant performance support. Managing and maintaining the infrastructure to support numerous possible applications in a large and complicated centralized networked resource environment for a variety of different clients raises many challenging operational issues.
Traditional centralized resource environments usually have set infrastructure support parameters even though there is usually a wide variety of equipment and applications involved. For example, equipment racks usually have predetermined power consumption and thermal dissipation limits. The power consumption and thermal dissipation limitations are often referred to as the power and thermal “budget”. The equipment in the racks is typically operated at a preset performance level that consumes a fixed amount of power and dissipates a fixed amount of heat. The amount of power consumed and heat dissipated by any particular piece of rack equipment is traditionally constant regardless of whether the benefit of the set predetermined performance level outweighs the impact of the power consumed and/or heat dissipated.
While the power consumed and the heat dissipated by a particular piece of rack equipment typically remains relatively constant, the applications running on any particular piece of equipment can vary over time. If the rack equipment is set predetermined anticipated average fixed performance level it can be inadequate to handle a complex or high “performance” application. Alternatively, if the application is a simple application the rack equipment can have significant resources sitting idle. Trying to predetermine an appropriate fixed performance level is also usually complicated by power and thermal budget concerns. The variety of different types of equipment typically included in centralized computing resource racks adds complexity in anticipating performance level settings that are compatible with cumulative power and thermal budget concerns. Further complicating the issue situations in which that more servers can be physically located or “housed” within a rack than can be cooled and power continuously provided within. The varying configuration of the rack equipment with different power and thermal profiles and the variety of possible applications with different performance requirements makes predetermining optimal settings very difficult.
Traditional attempts at addressing varying application performance requirements often introduce other complications. For example, some traditional approaches involve partially loading a rack in an effort to allocate more of a power thermal budget to fewer devices. However, in addition to potential for the rack equipment with fixed high performance setting to site idle during lower performance applications it also often results in consumption of expensive data center floor space since more equipment racks are required for the equipment. Traditional rack equipment settings are also sometimes set to permit rack equipment to run at relatively high temperatures, which usually increases the failure rate of the components housed in the equipment rack. Conversely, traditional attempts sometimes deploy more lower power rack equipment (e.g., servers) in an attempt to conserve floor space. However, the performance limited power rack equipment typically have restricted capacity and/or functionality that are impact high performance application results.
Traditional attempts at varying performance in conventional centralized resource centers are usually labor intensive and difficult. In addition, attempting to manually address problems that can arise is often complicated and the response time slow when compared to the speed at which application changes occur. Furthermore, many traditional rack equipment manual performance variation attempts do not promote efficient use of a power consumption and heat dissipation budget.